57 results about "CTD" patented technology

The invention relates to an ocean internal temperature inversion method fusing PIES and sea surface multi-source parameters, and belongs to the technical field of ocean environment parameter inversion and ocean dynamics observation. Comprising the following steps: constructing a nonlinear empirical model (Multi-RF) of seawater temperature inversion by using a random forest algorithm and fusing data such as a historical observation temperature-salinity profile observed by a temperature-salinity-depth meter (CTD) and sea surface multi-source parameters; and inverting the seawater temperature through a Multi-RF model by using the sound propagation time and multi-source sea surface data calculated by the PIES. Compared with a traditional empirical mode mapping (GEM) model, the Mull-RF model provided by the invention has the advantages that on one hand, the Mull-RF model utilizes fewer samples to establish a mapping relation between the seawater temperature and multiple parameters, and the problem of sample shortage in construction of a seawater temperature inversion model fusing sea surface multiple parameters and PIES observation sound propagation time is solved; on the other hand, compared with polynomial fitting in a GEM model, the random forest adopted by the Multi-RF model can achieve nonlinear fitting from multiple parameters to the seawater temperature, and therefore the temperature inversion precision is remarkably improved.

The invention discloses an XBT-T5 observation data deviation analysis and temperature correction method, and relates to the technical field of ocean observation, and the method comprises the steps: obtaining the original temperature observation data of a target sea area based on an XBT-T5 front-end temperature sensor, calculating the depth, generating a temperature-depth profile, and constructing a temperature-depth database in combination with historical depth observation archive data; the method comprises the following steps: synchronously acquiring temperature and depth data by using a CTD temperature-salinity-depth instrument, and carrying out association pairing with a temperature-depth database to generate an XBT-T5 observation data deviation field under the same spatial-temporal scale; based on generation of an XBT-T5 observation data deviation field under the same spatial-temporal scale, designing a dual-channel adaptive encoder, and constructing a temperature-depth collaborative correction model; and according to the temperature-depth collaborative correction model, coupling the temperature-depth database and generating an XBT-T5 observation data deviation field under the same spatial-temporal scale, thereby realizing XBT-T5 observation data deviation analysis and temperature correction. The method improves the quality and application value of observation data.

The invention discloses an AE-LSTM-based underwater glider CTD sampling abnormal data detection method and system and a storage medium, and belongs to the technical field of ocean exploration, the detection method comprises the steps that an AE-LSTM hybrid model is constructed, and the model comprises a bidirectional long short-term memory network Bi-LSTM encoder and a unidirectional LSTM decoder; preprocessing the original CTD time sequence data, wherein the preprocessing comprises edge auxiliary point expansion and upper-layer ocean data density interpolation; inputting the preprocessed data into the AE-LSTM hybrid model for encoding and decoding, and generating reconstructed data; calculating a reconstruction error of the original data and the reconstruction data; and dynamically setting a threshold value by a POT method based on an extreme value theory EVT, and judging abnormal data according to the reconstruction error. The method has the advantages of being high in detection precision, high in adaptability, low in misjudgment rate, suitable for large-scale automatic processing and the like, the requirement for obtaining high-quality observation data in the complex dynamic marine environment can be effectively met, and reliable data guarantee is provided for marine scientific research, environmental monitoring and the like.

The invention relates to the technical field of offshore wind plant area marine environment monitoring, and discloses an offshore wind plant wide-area environment panoramic sensing method and system, and the method comprises the steps: S1, laying seabed bottom type comprehensive observation equipment and low-altitude meteorological observation equipment in a target area of an offshore wind plant; the seabed bottom type comprehensive observation equipment comprises an acoustic Doppler flow velocity profiler, a tide gauge and a temperature-salinity-depth instrument; the low-altitude meteorological observation equipment comprises a meteorological station and a wind measuring radar; s2, collecting field data through the seabed bottom type comprehensive observation equipment and the low-altitude meteorological observation equipment, collecting remote sensing data through the meteorological remote sensing satellite, performing inversion on the remote sensing data to obtain remote sensing inversion data, and transmitting the field data and the remote sensing inversion data to a software processing platform and the like; according to the method and the system, collaborative acquisition, precise processing and fusion prediction of air-sky-sea multi-dimensional data can be realized, and the comprehensiveness and the accuracy of offshore wind plant environment perception are improved.

The invention provides a parameter self-adaptive adjustment method for a semi-automatic calibration system of a thermohaline depth gauge, and belongs to the technical field of marine survey. The parameter self-adaptive adjustment method comprises the following steps: determining the length of a calibration window by self-adaptive clustering; acquiring original data of the sensor and extracting features; calculating a measurement deviation value; establishing an association data set of the environmental factors and the deviations; constructing and training a multilayer neural network model; dynamically optimizing the calibration window length; verifying and optimizing the parameter adaptive adjustment model; applying an error decomposition function to separate different types of errors; generating compensation parameters by using a deep residual attention network model; and deploying the optimization model into the system to realize real-time adaptive adjustment. According to the method, adaptive data processing and deep learning technologies are combined, the problem that the measurement precision of the temperature-salinity-depth instrument is reduced in a complex and changeable environment is effectively solved through sensor parameter real-time optimization and error accurate compensation, and the reliability and accuracy of ocean observation data are improved.

The invention relates to the technical field of instrument error monitoring, and discloses an environment monitoring instrument tendency error analysis system which comprises a multi-dimensional acquisition module and an intelligent analysis module. According to the tendentiousness error analysis system for the temperature-salinity-depth instrument, conductivity data, temperature data and water depth data of a measured water body are obtained through a multi-dimensional acquisition module and classified to form a data set, an intelligent analysis module analyzes time differences among different physical parameters, generates a time difference data set, analyzes fluctuation ranges of the different physical parameters, generates a fluctuation data set, and analyzes the tendentiousness error of the temperature-salinity-depth instrument. The intelligent analysis module analyzes the authenticity of each measurement result, generates a credibility index, dynamically evaluates the credibility of single measurement, can adapt to the monitoring requirements of more complex environments, accurately diagnoses error sources, sets a threshold value of a fixed range, and can accurately diagnose the error sources; the influence factors causing the tendency error of the thermohaline depth gauge and the authenticity of the measurement result are evaluated, corresponding management suggestions are output, and the automatic operation and maintenance support practicability is high.

The invention provides an in-cabin and out-cabin dual-temperature compensation temperature-salinity-depth instrument, which belongs to the technical field of ocean measurement devices and comprises a control chip, a temperature sensing device, a pressure-depth sensing device, a conductivity measurement device, a data storage device and a communication device. The system measures the temperature inside and outside the cabin simultaneously through the inner and outer temperature sensors, dynamically adjusts compensation parameters by combining an adaptive temperature difference compensation function, and realizes accurate temperature measurement. And through an OceanParamNet deep learning model, the anomaly detection and parameter prediction capabilities are enhanced. The model is based on a hybrid architecture of a variational auto-encoder and a bidirectional long-short-term memory network, and introduces a sparse attention mechanism and physical constraint regularization. The system can adaptively adjust the sampling frequency according to the depth change rate, optimizes the power consumption, guarantees the data quality, and effectively solves the technical problem that the marine parameter measurement precision is not stable under the dynamic change condition of the temperature difference inside and outside the cabin.

The invention discloses a multi-layer synchronous micro-plastic observation system and method based on ocean frontal surface tracking. The system comprises a frontal surface tracking ship and a frontal surface crossing observation ship; a Manta trawl net and a sampling pump are installed in the sampling device; an ADCP and a CTD are installed in the latter. The method comprises the following steps: selecting one or more legs on a frontal surface; carrying out micro-plastic tracking navigation along the frontal surface direction of the frontal surface tracking ship; collecting micro-plastic particles on a surface layer and a vertical section; carrying out cross-frontal surface navigation perpendicular to the frontal surface direction of the cross-frontal surface observation ship; calculating a salinity horizontal gradient based on interpolation, calculating horizontal flow velocity divergence, and then performing observation data fitting to obtain a corresponding relation between frontal surface strength and micro-plastic abundance. According to the invention, key data are synchronously acquired through a double-working ship system, so that the acquisition of micro-plastics at the ocean frontal surface and the observation of physical elements are realized, and the influence and action relationship of the physical elements of the ocean frontal surface on the micro-plastics is established.

The invention relates to the technical field of ocean observation, in particular to a CTD observation data anomaly detection and correction method, which comprises an anomaly detection stage and an anomaly correction stage, in the anomaly detection stage, an RS-MLP combined mixed detection model is adopted to carry out anomaly identification on original CTD data; and in the abnormity correction stage, time sequence prediction correction is carried out on the detected abnormal data based on the STL-LSTM model. In the aspect of detection precision, the RS-MLP method integrates domain knowledge and a data driving method, the recall rate is improved while the high precision rate is ensured, and the omission ratio is effectively reduced; in the aspect of correction quality, the STL-LSTM method extracts internal structure features of data through time sequence decomposition, and realizes high-quality anomaly correction in combination with the time sequence modeling capability of LSTM; in the aspect of calculation efficiency, the two-stage design of the RS-MLP not only ensures the detection precision, but also improves the calculation efficiency; the rapid screening of RS reduces the amount of data that needs to be processed by MLP.

This invention relates to a data processing system for CTD (Conductivity, Temperature, Depth) instrument observations, comprising: building a fully domestically developed technology foundation, employing a combination of Spring Boot, MyBatis-Plus, DM database, and Vue3 to achieve domestic adaptation of the front-end, back-end, and database; incrementally accessing and storing raw CTD data files based on the technology foundation, generating dual traceability identifiers of FileInfoID and DataID; performing multi-step sequential processing on the stored data, including zero-error correction, outlier filtering, data smoothing, salt density and acoustic calculation, and standard file generation, with each step's algorithm optimized for the characteristics of domestically produced CTD data; storing raw data, intermediate data, and output data through a multi-level data table design, utilizing traceability identifiers to achieve full-process correlation and traceability, and providing visual display and standardized output. This invention achieves fully domestic processing from data access to output, effectively improving the accuracy and traceability of data processing and ensuring marine data security.

The invention discloses an empirical sound velocity error modeling compensation method based on a neural network, which belongs to the technical field of ocean observation data processing, is used for sound velocity error modeling compensation, and comprises the following steps: carrying out data alignment on observation data of CTD and SVP; comparing and analyzing the difference between the sound velocity value calculated by the CTD through an empirical formula and the sound velocity value directly measured by the SVP, and evaluating the correlation through a Pearson's correlation coefficient; constructing a nonlinear regression model by adopting a machine learning algorithm, and continuously optimizing model parameters; establishing a model evaluation index to evaluate the compensated sound velocity precision; machine learning setting parameters are fed back and corrected, and the compensation model is continuously optimized. Compared with the prior art, the method has the advantages that the accuracy of CTD sound velocity calculation is improved, and the established mathematical model has universality and expandability and can be adjusted and optimized according to different sea areas and different measurement conditions; the marine observation cost can be reduced, and the utilization efficiency of the marine observation data can be improved.

The application relates to the technical field of marine environment monitoring, and discloses a marine environment multi-parameter monitoring buoy, which comprises a base, a CTD sensor arranged at the underwater position of the base and internally integrated with a wave sensor; a support, a wind direction and speed sensor, an air pressure sensor and an air temperature and humidity sensor being mounted at the top of the support; a photovoltaic panel angle adjusting module; a photovoltaic panel frame; a cleaning assembly; and a self-adaptive angle adjusting module. The longitudinal rotating scraper is separated from seawater after rotating, so that the long-term immersion of the scraper in seawater is avoided, the corrosion of the scraper is accelerated, meanwhile, the biological organisms such as barnacles in the seawater cannot be attached and grown on the surface of the scraper, and the service life of the scraper is prolonged. The space posture of the mounting disc is adjusted through the cooperation of the telescopic adjustment, the corresponding push rod is driven to independently extend and retract, the mounting disc always keeps a dynamic horizontal state when the buoy swings with waves, and the requirements of the meteorological observation specification on the wind direction and speed measurement accuracy are met.

The invention discloses a coral reef ecosystem community carbon sequestration evaluation method based on a Lagrange drift trajectory, and belongs to the field of marine ecological environment monitoring and carbon cycle research. The device comprises a drift tracking module, a CTD module, a hydrochemical parameter acquisition module, a depth instrument, a waterproof system and a buoyancy system, all the modules are integrated in the waterproof system, the buoyancy system is connected with the exterior of the waterproof system, and the modules are in signal connection to realize data transmission; according to the method, a Lagrange drift path and a time interval are recorded through the drift tracking module, the CTD module collects the temperature and salinity of a water body, the hydrochemical parameter collection module obtains total alkalinity and dissolved inorganic carbon and calculates the variable quantity of the total alkalinity and the dissolved inorganic carbon, and the depth instrument monitors the average water depth; the COexchange flux is calculated in combination with meteorological data, and then the net calcification rate and the net photosynthesis rate are obtained through a preset formula.

The application discloses a multilayer synchronous micro-plastic observation system and method based on ocean front tracking, which comprises a front tracking ship and a cross-front observation ship; the front tracking ship is provided with a Manta trawl net and a sampling pump; and the cross-front observation ship is provided with an ADCP and a CTD. The method comprises the following steps: selecting one or more voyage sections on the front, performing micro-plastic tracking voyage along the front direction by the front tracking ship, collecting micro-plastic particles in the surface layer and vertical section, performing cross-front voyage perpendicular to the front direction by the cross-front observation ship, measuring key physical parameters of the front intensity, counting the micro-plastic abundance of each voyage section, calculating the salt density horizontal gradient based on interpolation, calculating the horizontal flow velocity divergence, and then performing observation data fitting to obtain the corresponding relationship between the front intensity and the micro-plastic abundance. The application realizes the collection of micro-plastics at the ocean front and the observation of physical elements by synchronously collecting key data through the double-ship system, and establishes the influence and action relationship of the ocean front physical elements on the micro-plastics.

The invention relates to a water mass classification accuracy judgment method, and the method comprises the steps: obtaining target sea area CTD observation data, and carrying out the standardization processing, and obtaining the standardized data; based on a K-means + + clustering algorithm, spatial clustering analysis is carried out on the standardized data with the multi-element features, the optimal cluster number is screened out, and spatial distribution of the water mass is obtained; combining the spatial distribution of the water mass with the multi-element features, and determining the category and spatial distribution of the water mass and the three-dimensional division result of the feature range of each element according to the optimal cluster number; and according to a principal component analysis method, the separation degree and the concentration degree of each type of water mass in a principal component space are detected so as to evaluate the accuracy of a three-dimensional division result. According to the method, three-dimensional water mass classification is realized, the space structure of the water mass in the target sea area can be comprehensively disclosed, the classification accuracy is objectively judged, and the influence of subjective factors on a water mass division result is effectively reduced.

This invention provides a positioning method for the main buoy of an acoustic underwater mooring, relating to the field of acoustic underwater mooring main buoy positioning technology. Specifically, it includes the following steps: using a pressure sensor to measure the pressure at the location of the main buoy, and then calculating the actual depth and settling displacement of the main buoy; indirectly calculating the sound velocity in seawater using a segmented CTD (Cyclic Transmission Device) on the mooring system, and using the sound velocity to calculate the distance between the main buoy and the anchored weight, thereby indirectly calculating the horizontal offset of the main buoy; measuring the seawater current velocity vector in the water area where the main buoy is located using an ADCP (Advanced Directional Phase Array); and installing a current-facing tail fin at the tail of the main buoy to ensure that the main buoy always faces the current, with the opposite direction of the seawater flow being the direction of the main buoy. The technical solution of this invention overcomes the problem of existing technologies being unable to accurately position the main buoy of an acoustic underwater mooring.

The utility model relates to the technical field of unmanned ships, in particular to a bimodal semi-submersible unmanned ship with an integrated turbulent flow observation system, which comprises an unmanned ship body, the unmanned ship body comprises a fore peak, an instrument and equipment compartment, a moon pool compartment, a rear battery compartment, a cabin, a stern compartment, a front battery compartment and a fuel tank compartment, a ballast water pump and a liquid level sensor for detecting the liquid level in the two front ballast tanks are respectively arranged in the two front ballast tanks, an integrated turbulent flow observation system integrated with a turbulent flow profile observation instrument, a lowering type acoustic Doppler ocean current profiler and a temperature-salinity-depth instrument is arranged in the moon pool tank, and a lifting mechanism is arranged above the cabin. An automatic opening and closing type cover plate is arranged at the bottom of the moon pool cabin, the device further comprises a controller, by carrying an integrated turbulence observation system, the problem that small-scale turbulence observation frequency is insufficient or time resolution is low is solved, and synchronous observation of turbulence and thermohaline depth / flow is achieved.

The present application relates to the technical field of offshore wind farm area marine environment monitoring, and discloses a kind of offshore wind farm wide area environment panoramic perception method and system, comprising: S1, in the target area of offshore wind farm layout seabed bottom type comprehensive observation equipment and low-altitude meteorological observation equipment;The seabed bottom type comprehensive observation equipment includes acoustic Doppler current profiler, tide gauge, temperature salinity depth instrument, and the low-altitude meteorological observation equipment includes weather station, wind radar;S2, through seabed bottom type comprehensive observation equipment, low-altitude meteorological observation equipment acquisition field data, through meteorological remote sensing satellite acquisition remote sensing data, remote sensing data is inverted to obtain remote sensing inversion data, and field data and remote sensing inversion data are transmitted to software processing platform etc.;The method and system can realize the collaborative collection of space-time-sea multidimensional data, accurate processing and fusion prediction, improve the comprehensiveness and accuracy of offshore wind farm environment perception.

The invention belongs to the technical field of ocean monitoring and sensors, and discloses a measuring method and device for correcting the actual ocean shallow profile temperature. The method comprises the following steps: obtaining the real emissivity of a calm water surface; inputting the wind speed wave height of the seawater in the actual sea condition, the wind speed of the air and the obtained actual emissivity of the calm water surface into a Monte Carlo emissivity correction model, outputting the corrected actual sea condition seawater emissivity, and taking the corrected actual sea condition seawater emissivity as a parameter for measuring the actual water surface temperature by a second IR infrared thermometer; and based on the obtained modified parameters of the real water surface temperature measured by the second IR infrared thermometer, carrying out actual sea surface temperature measurement by arranging thermosensitive chains with different scales. The method is obviously superior to the prior art (the satellite is 0.3-1K, the shipborne infrared is 0.05-0.1 K, and the anchor system CTD is 0.002 K and is larger than or equal to 5cm on average).

The application discloses a temperature time domain alignment and error compensation method of a temperature-salinity-depth instrument, and relates to the technical field of temperature-salinity-depth instrument measurement, and the specific steps are as follows: firstly, the original temperature sequence of the temperature-salinity-depth instrument and the temperature true value sequence of a high-precision reference instrument are synchronously collected, time domain alignment of the original temperature sequence is completed through root mean square error minimization search, based on the aligned temperature sequence, the first-order temperature change rate and the second-order temperature change rate dynamic characteristics are calculated, and a multi-dimensional feature vector is constructed; the actual residual error of the aligned temperature sequence and the temperature true value sequence is taken as a learning target, a random forest nonlinear regression model is trained, a residual error compensation amount is fitted, and a final corrected temperature sequence is output. Compared with the traditional linear model method, the root mean square error of temperature measurement of the model method is significantly reduced, the nonlinear dynamic error under the strong dynamic variable temperature working condition is effectively inhibited, the measurement precision and the robustness of the temperature-salinity-depth instrument are improved, and the application requirements of ocean observation are met.

The utility model provides a sectional type seabed base suitable for coral island power environment monitoring, which belongs to the field of coral island sea area monitoring equipment and comprises a hollow cylinder and an inclined tube single-side support assembly, a universal ring is arranged at the center of the hollow cylinder, a hollow circular ring is arranged inside the bottom of the hollow cylinder, and the inclined tube single-side support assembly is connected with the universal ring. A plurality of cylindrical vertical rod guardrails are arranged at the lower end of the hollow circular ring, a cylindrical bottom support is arranged at the bottom ends of the cylindrical vertical rod guardrails, an acoustic Doppler flow velocity profiler is arranged on the universal ring, and a hollow disc is arranged on the outer wall of the hollow cylinder; the upper ends of the inclined tube single-side support assemblies are connected with the hollow disc through inclined tube upper steel plates, inclined tube bottom perforated round steel plates are arranged at the lower ends of the inclined tube single-side support assemblies, and the two inclined tube single-side support assemblies are respectively provided with a wave tide meter, a temperature-salinity-depth meter and a turbidity meter. The device is suitable for being laid on the coral reef shallow water coast through a small civil ship, the instrument and the seabed base are detachable, and the device is convenient to carry and deploy.

This invention relates to the field of marine acoustic modeling and underwater acoustic environment forecasting, and in particular to an integrated method for dynamic extraction of Kuroshio frontal features and sound field modeling based on publicly available data. Through the integrated design of dynamic encryption of publicly available data, frontal feature quantization and PINNs modeling, this invention greatly reduces the sound field forecasting error of Kuroshio frontal regions and reduces the cost of CTD measured data.

The present invention provides a temperature-salinity-depth instrument measurement system, which belongs to the field of deep-sea detection technology. The system comprises a plurality of temperature-salinity-depth instruments and a host computer. The temperature-salinity-depth instruments are used to detect the electrical conductivity and temperature of water bodies at different depths in a plurality of ocean regions in a sea area to be measured. The temperature-salinity-depth instruments are provided with a magnetic weight for sinking in water and an energized demagnetization module, which is used to sink to the seabed when the temperature-salinity-depth instruments are deployed and to separate the magnetic weight. The host computer is provided with a deployment allocation module, a data receiving module, a data analysis module and an equipment tracking module. The deployment allocation module is used to determine the deployment position of each temperature-salinity-depth instrument according to ocean parameters of the sea area to be measured; the data receiving module is used to receive detection data collected by each temperature-salinity-depth instrument; the data analysis module is used to calculate the electrical conductivity and temperature model of all positions in the sea area to be measured based on the data collected by all the temperature-salinity-depth instruments; and the equipment tracking module is used to calculate the current position of the temperature-salinity-depth instrument to facilitate recovery of the temperature-salinity-depth instrument by recovery personnel.

The present invention discloses an optical fiber ocean temperature, salinity, and depth multi-parameter integrated sensor based on cumulative optical fiber length sensitization. The sensor is characterized in that it comprises a hatch cover module, a depth measurement module, a temperature module measurement block, and a salinity measurement module, which are sequentially connected by single-mode optical fibers along the direction from signal input to signal output; the hatch cover module comprises a sensor top cover and an optical fiber hatch connector; the depth measurement module comprises an inner protective shell, a depth-sensitive optical fiber ring, a depth transducer disc, and seawater; the temperature measurement module comprises thermal oil, a temperature-sensitive optical fiber ring, and a temperature transducer core shaft; and the salinity measurement module comprises an outer protective shell, an outer hollow structure, a salt-sensitive optical fiber structure, and an inner core shaft structure. The present invention has the beneficial effects of establishing a temperature, salinity, and depth sensing model in which "one optical fiber measures multiple parameters," thereby solving the problems of low versatility, low sampling rate, low integration, and poor long-term stability of temperature, salinity, and depth meters.

The application discloses a modular split-beam biomass detection system and method, comprising a split-beam fish finder hardware unit, a main control unit, a communication unit, a shore-based monitoring terminal, a mounting bracket, a shipborne display terminal and an external sensor assembly. The mounting bracket is provided with an underwater transducer mounting portion and a cable fixing portion. A transducer array, an attitude sensor and a temperature-salinity-depth instrument are arranged based on the same mounting bracket. After being limited by the cable fixing portion, an acoustic cable, an attitude sensor cable and a temperature-salinity-depth instrument cable are connected to the main control unit. The main control unit connects acoustic data, sensor data, communication data and display data through an interface board, a backboard connector and a partition interface portion, and sends biomass resource abundance evaluation data to the shore-based monitoring terminal through the communication unit, and is suitable for shipborne underwater acoustic biomass continuous detection, on-site processing and remote monitoring.

The application discloses a reciprocating CTD profile detection device, which comprises a machine body, a probe module, a first piston arranged in a first cabin, a second piston arranged between the first cabin and a second cabin to isolate the first cabin from the second cabin, wherein the first cabin stores seawater, the second cabin stores a chemical reaction liquid agent capable of generating gas with chemical reaction particles, a first electromagnetic valve, a second electromagnetic valve, a third electromagnetic valve, a first driving assembly arranged to drive the first piston to move in the first cabin, and a control module. The device realizes multiple sinking and floating reciprocating movements, and can repeatedly complete profile detection on a preset sea area multiple times. In the process, the working ship does not need to wait for a long time, and can sail to a next sea area position to release and detect after releasing, thereby saving time cost and improving work efficiency.

The present application relates to the technical field of marine observation and data processing, in particular to a kind of Yellow Sea cold water mass submesoscale structure observation method and its observation system, comprising the following steps: step one: data acquisition;Obtain multiple profile data by the temperature-salinity-depth instrument carried by wave energy profile float, import profile data into observation system, form profile data txt file, as raw data;Step two: feature recognition and three-dimensional reconstruction;Raw data is processed using python program, to obtain temperature-depth-time array and salinity-depth-time array;Feature recognition, analysis and reconstruction of three-dimensional structure of submesoscale phenomenon are carried out jointly, and the observation of the submesoscale structure of the Yellow Sea cold water mass is realized through the reconstructed three-dimensional structure.The present application realizes the dynamic monitoring of the boundary submesoscale process of the Yellow Sea cold water mass by integrating wave energy profile float and satellite remote sensing technology.

The invention relates to the technical field of ocean internal wave monitoring, and discloses an internal wave inversion method and system based on high-frequency tide level data and satellite remote sensing fusion, and the method comprises the steps: S1, collecting SAR satellite remote sensing data, five-minute sampling interval tide level data, and CTD density profile data, and carrying out the preprocessing; s2, performing space-time alignment on SAR satellite remote sensing data, 5-minute sampling interval tide level data and CTD density profile data; s3, variational mode decomposition is adopted, the preprocessed five-minute sampling interval tide level data are decomposed, and internal wave characteristic modes and the like are screened; according to the method and the system, the spatial resolution of high-precision inversion of multiple parameters such as amplitude, period, propagation speed, direction and energy dissipation is improved to ten meters, the time resolution is optimized to be within one day, the amplitude measurement precision is improved to 0.1 meter, and the method and the system are remarkably superior to the internal wave amplitude measurement precision and the time resolution of an existing SAR satellite.

The invention discloses a temperature time domain alignment and error compensation method for a temperature-salinity-depth instrument, and relates to the technical field of temperature-salinity-depth instrument measurement, and the method comprises the following specific steps: firstly, synchronously collecting an original temperature sequence of the temperature-salinity-depth instrument and a temperature truth value sequence of a high-precision reference instrument, and carrying out the minimum search of a root-mean-square error; time domain alignment of the original temperature sequence is completed, dynamic features of a first-order temperature change rate and a second-order temperature change rate are calculated based on the aligned temperature sequence, and a multi-dimensional feature vector is constructed; and training a random forest nonlinear regression model by taking an actual residual error of the aligned temperature sequence and the temperature truth value sequence as a learning target, fitting a residual error compensation amount, and outputting a final corrected temperature sequence. Compared with a traditional linear model method, the model method provided by the invention has the advantages that the root-mean-square error of temperature measurement is obviously reduced, the nonlinear dynamic error under a strong dynamic temperature change working condition is effectively inhibited, the measurement precision and robustness of the thermohaline depth gauge are improved, and the application requirements of ocean observation are met.

The application relates to the technical field of instrument error monitoring, and discloses an environmental monitoring instrument tendency error analysis system which comprises a multidimensional acquisition module and an intelligent analysis module. The thermosalinograph tendency error analysis system obtains the conductivity data, temperature data and water depth data of a measured water body through the multidimensional acquisition module, classifies and forms a data set, the intelligent analysis module analyzes the time difference between different physical parameters, generates a time difference data set, analyzes the fluctuation range of different physical parameters, generates a fluctuation data set, quickly locates the measurement deviation, the intelligent analysis module analyzes the authenticity of each measurement result, generates a credibility index, dynamically evaluates the credibility of single measurement, can adapt to the monitoring requirements of more complex environments, accurately diagnoses the error source, the intelligent analysis module sets a fixed range threshold, evaluates the influence factors leading to the thermosalinograph tendency error and the authenticity of the measurement result, and outputs corresponding management suggestions, and the automatic operation and maintenance has high practicability.